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A phenomenological model for COVID-19 data taking into account neighboring-provinces effect and random noise
dc.contributor.author | Calatayud, Julia | |
dc.contributor.author | Jornet, Marc | |
dc.contributor.author | Mateu, Jorge | |
dc.date.accessioned | 2023-02-10T20:08:02Z | |
dc.date.available | 2023-02-10T20:08:02Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | CALATAYUD, Julia; JORNET, Marc; MATEU, Jorge. A phenomenological model for COVID‐19 data taking into account neighboring‐provinces effect and random noise. Statistica Neerlandica, 2023, 77, 2, p. 146-155 | ca_CA |
dc.identifier.issn | 0039-0402 | |
dc.identifier.issn | 1467-9574 | |
dc.identifier.uri | http://hdl.handle.net/10234/201621 | |
dc.description.abstract | We model the incidence of the COVID-19 disease during the first wave of the epidemic in Castilla-Leon (Spain). Within-province dynamics may be governed by a generalized logistic map, but this lacks of spatial structure. To couple the provinces, we relate the daily new infec- tions through a density-independent parameter that entails positive spatial correlation. Pointwise values of the input parameters are fitted by an optimization procedure. To accommodate the significant variability in the daily data, with abruptly increasing and decreasing magnitudes, a random noise is incorporated into the model, whose parameters are calibrated by maximum like- lihood estimation. The calculated paths of the stochastic response and the probabilistic regions are in good agreement with the data. | ca_CA |
dc.format.extent | 9 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Wiley | ca_CA |
dc.relation.isPartOf | Statistica Neerlandica, 2023, 77, 2 | ca_CA |
dc.rights | "This is the pre-peer reviewed version of the following article: CALATAYUD, Julia; JORNET, Marc; MATEU, Jorge. A phenomenological model for COVID‐19 data taking into account neighboring‐provinces effect and random noise. Statistica Neerlandica, 2023, 77, 2, which has been published in final form at https://doi.org/10.1111/stan.12278. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions." | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | COVID-19 infections | ca_CA |
dc.subject | generalized logistic differential equation | ca_CA |
dc.subject | parameter calibration | ca_CA |
dc.subject | spatial correlation | ca_CA |
dc.subject | stochastic modeling | ca_CA |
dc.title | A phenomenological model for COVID-19 data taking into account neighboring-provinces effect and random noise | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1111/stan.12278 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://onlinelibrary.wiley.com/doi/full/10.1111/stan.12278 | ca_CA |
dc.type.version | info:eu-repo/semantics/submittedVersion | ca_CA |
project.funder.name | Universitat Jaume I | ca_CA |
project.funder.name | Generalitat Valenciana | ca_CA |
project.funder.name | Ministerio de Ciencia e Innovación | ca_CA |
oaire.awardNumber | PID2019‐107392RB‐I00 | ca_CA |
oaire.awardNumber | AICO/2019/198 | ca_CA |
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